| from typing import List |
|
|
| from transformers import PretrainedConfig |
|
|
| """ |
| The configuration of a model is an object that |
| will contain all the necessary information to build the model. |
| |
| The three important things to remember when writing you own configuration are the following: |
| |
| - you have to inherit from PretrainedConfig, |
| - the __init__ of your PretrainedConfig must accept any kwargs, |
| - those kwargs need to be passed to the superclass __init__. |
| """ |
|
|
|
|
| class ResnetConfig(PretrainedConfig): |
|
|
| """ |
| Defining a model_type for your configuration (here model_type="resnet") is not mandatory, |
| unless you want to register your model with the auto classes (see last section).""" |
|
|
| model_type = "rgbdsod-resnet" |
|
|
| def __init__( |
| self, |
| block_type="bottleneck", |
| layers: List[int] = [3, 4, 6, 3], |
| num_classes: int = 1000, |
| input_channels: int = 3, |
| cardinality: int = 1, |
| base_width: int = 64, |
| stem_width: int = 64, |
| stem_type: str = "", |
| avg_down: bool = False, |
| **kwargs, |
| ): |
| if block_type not in ["basic", "bottleneck"]: |
| raise ValueError( |
| f"`block_type` must be 'basic' or bottleneck', got {block_type}." |
| ) |
| if stem_type not in ["", "deep", "deep-tiered"]: |
| raise ValueError( |
| f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}." |
| ) |
|
|
| self.block_type = block_type |
| self.layers = layers |
| self.num_classes = num_classes |
| self.input_channels = input_channels |
| self.cardinality = cardinality |
| self.base_width = base_width |
| self.stem_width = stem_width |
| self.stem_type = stem_type |
| self.avg_down = avg_down |
| super().__init__(**kwargs) |
|
|
|
|
| if __name__ == "__main__": |
| """ |
| With this done, you can easily create and save your configuration like |
| you would do with any other model config of the library. |
| Here is how we can create a resnet50d config and save it: |
| """ |
| resnet50d_config = ResnetConfig( |
| block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True |
| ) |
| resnet50d_config.save_pretrained("custom-resnet") |
|
|
| """ |
| This will save a file named config.json inside the folder custom-resnet. |
| You can then reload your config with the from_pretrained method: |
| """ |
| resnet50d_config = ResnetConfig.from_pretrained("custom-resnet") |
|
|
| """ |
| You can also use any other method of the PretrainedConfig class, |
| like push_to_hub() to directly upload your config to the Hub. |
| """ |
|
|
